A Study of Neuro-fuzzy System in Approximation-based Problems

The Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference System (FIS) has attracted a growing interest of researchers in various scientific and engineering areas due to the growing need for adaptive intelligent systems to solve real world problems. ANN learns by adjusting the interconnections between layers. FIS is a popular computing framework based on the concept of fuzzy set theory and fuzzy if-then rules. The advantages of the combination of ANN and FIS are apparent. This paper implements a hybrid neuro-fuzzy system underlying ANFIS (Adaptive NeuroFuzzy Inference System), a fuzzy inference system implemented in the framework of neural networks. The motivation stems from a desire to achieve performance in terms of accuracy and several simulations studies regarding the determination of the optimal number of membership functions have been done. In our simulations, we utilize the ANFIS architecture to model nonlinear functions. In addition, the effects of using different types of membership functions were compared. Based upon numerical evidence, some general guidelines for choosing the number of membership function have been proposed. To experiment with the technique that allows the combination of neural network and fuzzy system, we have implemented ANFIS to a real world application (Phytoplankton concentration problem); and yielding good results.